Now showing 1 - 10 of 42
  • Publication
    Automatic Grammar Complexity Reduction in Grammatical Evolution
    Grammatical Evolution is an automatic programming system, where a population of binary strings is evolved, from which phenotype strings are generated through a mapping process, that employs a grammar to define the syntax of such output strings. This paper presents a study of the effect of grammar size and complexity on the performance of the system. A simple method to reduce the number of non-terminal symbols in a grammar is presented, along with the reasoning behind it. Results obtained on a series of problems suggest that performance can be increased with the approach presented.
  • Publication
    Moving Towards Big Data Scalability with the Grammatical Evolution System
    The increasing presence of connected computing devices presents a formidable opportunity for the scalability of GP-like systems. In this work, we propose and partially implement a framework to deploy one such system, Grammatical Evolution, across a highly heterogeneous, asynchronous network of computing devices. We work towards a system combining the dynamic nature of such a network with the inherent adaptability of evolutionary systems. Early experiments are designed, using the open-source million song dataset.
  • Publication
    Designing a Massive Dataset Framework for the Grammatical Evolution System
    In this study, a GE-framework is built, in an effort to apply it to huge datasets. Combining statistical techniques such as appropriate error measures and data splitting, population-based improvements such as mass parallelisation, and even specific techniques such as grammar design and repeat management, GE is applied for the first time to massive datasets, such as the Higgs dataset (eleven million samples).
  • Publication
    Introducing Grammar Based Extensions for Grammatical Evolution
    (IEEE, 2006-07-21) ;
    This paper presents a series of extensions to standard Grammatical Evolution. These grammar-based extensions facilitate the exchange of knowledge between genotype and phenotype strings, thus establishing a better correlation between the search and solution spaces, typically separated in Grammatical Evolution. The results obtained illustrate the practical advantages of these extensions, both in terms of convenience and potential increase in performance.
  • Publication
    Dynamic ant : introducing a new benchmark for genetic programming in dynamic environments
    (University College Dublin. School of Computer Science and Informatics, 2011-04-14) ; ; ; ;
    In this paper we present a new variant of the ant problem in the dynamic problem domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.
  • Publication
    A GAuGE Approach to Learning DFA from Noisy Samples
    This paper describes the adaptation of the GAuGE system to classify binary sequences generated by random DFA. Experiments were conducted, which, although not highly successful, illustrate the potential of applying GAuGE like systems to this problem domain.
  • Publication
    Dynamic environments can speed up evolution with genetic programming
    (University College Dublin. School of Computer Science and Informatics, 2011) ; ;
    We present a study of dynamic environments with genetic programming to ascertain if a dynamic environment can speed up evolution when compared to an equivalent static environment. We present an analysis of the types of dynamic variation which can occur with a variable-length representation such as adopted in genetic programming identifying modular varying, structural varying and incremental varying goals. An empirical investigation comparing these three types of varying goals on dynamic symbolic regression benchmarks reveals an advantage for goals which vary in terms of increasing structural complexity. This provides evidence to support the added difficulty variable length representations incur due to their requirement to search structural and parametric space concurrently, and how directing search through varying structural goals with increasing complexity can speed up search with genetic programming.
  • Publication
    Investigating Degenerate Code and Gene Dependency in the GAuGE System
    This paper explores the topics of gene dependency and degenerate code, and their combined effect in the GAuGE system, a recently introduced position-independent genetic algorithm. To do so, a simulation was used to calculate the average position specifications of all genotype individuals, and the effect of degnerate code on these averages. The results obtained so far suggest that the introduction of degenerate code loosens the dependency between the position coding genes in each individual.
  • Publication
    Genetic Algorithms using Grammatical Evolution
    (University of Limerick, 2006-09)
    This thesis proposes a new representation for genetic algorithms, based on the idea of a genotype to phenotype mapping process. It allows the explicit encoding of the position and value of all the variables composing a problem, therefore disassociating each variable from its genotypic location. The GAuGE system (Genetic Algorithms using Grammatical Evolution) is developed using this mapping process. In a manner similar to Grammatical Evolution, it ensures that there is no under- nor over-specification of phenotypic variables, therefore always producing syntactically valid solutions. The process is simple to implement and independent of the search engine used; in this work, a genetic algorithm is employed. The formal definition of the mapping process, used in this work, provides a base for analysis of the system, at different levels. The system is applied to a series of benchmark problems, defining its main features and potential problem domains. A thorough analysis of its main characteristics is then presented, including its interaction with genetic operators, the effects of degeneracy, and the evolution of representation. This in-depth analysis highlights the system’s aptitude for relative ordering problems, where not only the value of each variable is to be discovered, but also their correct permutation. Finally, the system is applied to the real-world problem of solving Sudoku puzzles, which are shown to be similar to instances of planning and scheduling problems, illustrating the class of problems for which GAuGE can prove to be a useful approach. The results obtained show a substantial improvement in performance, when compared to a standard genetic algorithm, and pave the way to new applications to problems exhibiting similar characteristics.
  • Publication
    Grammar Defined Introns: An Investigation Into Grammars, Introns, and Bias in Grammatical Evolution
    (Morgan Kauffman, 2001-07-11) ; ;
    We describe an investigation into the design of different grammars on Grammatical Evolution. As part of this investigation we introduce introns using the grammar as a mechanism by which they may be incorporated into Grammatical Evolution. We establish that a bias exists towards certain production rules for each non-terminal in the grammar, and propose alternative mechanisms by which this bias may be altered either through the use of introns, or by changing the degeneracy of the genetic code. The benefits of introns for Grammatical Evolution are demonstrated experimentally.